arXiv - CS - Mathematical Software最新文献_第5页

Scalable Automated Verification for Cyber-Physical Systems in Isabelle/HOL 用 Isabelle/HOL 对网络物理系统进行可扩展的自动验证

arXiv - CS - Mathematical Software Pub Date : 2024-01-22 DOI: arxiv-2401.12061

Jonathan Julián Huerta y Munive, Simon Foster, Mario Gleirscher, Georg Struth, Christian Pardillo Laursen, Thomas Hickman

引用次数: 0

Automated Completion of Statements and Proofs in Synthetic Geometry: an Approach based on Constraint Solving 自动完成合成几何中的语句和证明：基于约束求解的方法

arXiv - CS - Mathematical Software Pub Date : 2024-01-22 DOI: arxiv-2401.11898

Salwa Tabet GonzalezUniversity of Strasbourg, Predrag JaničićUniversity of Belgrade, Julien NarbouxUniversity of Strasbourg

引用次数: 0

PlasmoData.jl -- A Julia Framework for Modeling and Analyzing Complex Data as Graphs PlasmoData.jl -- 以图表形式建模和分析复杂数据的 Julia 框架

arXiv - CS - Mathematical Software Pub Date : 2024-01-21 DOI: arxiv-2401.11404

David L Cole, Victor M Zavala

{"title":"PlasmoData.jl -- A Julia Framework for Modeling and Analyzing Complex Data as Graphs","authors":"David L Cole, Victor M Zavala","doi":"arxiv-2401.11404","DOIUrl":"https://doi.org/arxiv-2401.11404","url":null,"abstract":"Datasets encountered in scientific and engineering applications appear in\u0000complex formats (e.g., images, multivariate time series, molecules, video, text\u0000strings, networks). Graph theory provides a unifying framework to model such\u0000datasets and enables the use of powerful tools that can help analyze,\u0000visualize, and extract value from data. In this work, we present PlasmoData.jl,\u0000an open-source, Julia framework that uses concepts of graph theory to\u0000facilitate the modeling and analysis of complex datasets. The core of our\u0000framework is a general data modeling abstraction, which we call a DataGraph. We\u0000show how the abstraction and software implementation can be used to represent\u0000diverse data objects as graphs and to enable the use of tools from topology,\u0000graph theory, and machine learning (e.g., graph neural networks) to conduct a\u0000variety of tasks. We illustrate the versatility of the framework by using real\u0000datasets: i) an image classification problem using topological data analysis to\u0000extract features from the graph model to train machine learning models; ii) a\u0000disease outbreak problem where we model multivariate time series as graphs to\u0000detect abnormal events; and iii) a technology pathway analysis problem where we\u0000highlight how we can use graphs to navigate connectivity. Our discussion also\u0000highlights how PlasmoData.jl leverages native Julia capabilities to enable\u0000compact syntax, scalable computations, and interfaces with diverse packages.","PeriodicalId":501256,"journal":{"name":"arXiv - CS - Mathematical Software","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139559796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Proceedings 14th International Conference on Automated Deduction in Geometry 第 14 届几何自动演绎国际会议论文集

arXiv - CS - Mathematical Software Pub Date : 2024-01-19 DOI: arxiv-2401.10725

Pedro QuaresmaUniversity of Coimbra, Portugal, Zoltán KovácsThe Private University College of Education of the Diocese of Linz, Austria

{"title":"Proceedings 14th International Conference on Automated Deduction in Geometry","authors":"Pedro QuaresmaUniversity of Coimbra, Portugal, Zoltán KovácsThe Private University College of Education of the Diocese of Linz, Austria","doi":"arxiv-2401.10725","DOIUrl":"https://doi.org/arxiv-2401.10725","url":null,"abstract":"ADG is a forum to exchange ideas and views, to present research results and\u0000progress, and to demonstrate software tools at the intersection between\u0000geometry and automated deduction. The conference is held every two years. The\u0000previous editions of ADG were held in Hagenberg in 2021 (online, postponed from\u00002020 due to COVID-19), Nanning in 2018, Strasbourg in 2016, Coimbra in 2014,\u0000Edinburgh in 2012, Munich in 2010, Shanghai in 2008, Pontevedra in 2006,\u0000Gainesville in 2004, Hagenberg in 2002, Zurich in 2000, Beijing in 1998, and\u0000Toulouse in 1996. The 14th edition, ADG 2023, was held in Belgrade, Serbia, in September 20-22,\u00002023. This edition of ADG had an additional special focus topic, Deduction in\u0000Education. Invited Speakers: Julien Narboux, University of Strasbourg, France\u0000\"Formalisation, arithmetization and automatisation of geometry\"; Filip Mari'c,\u0000University of Belgrade, Serbia, \"Automatization, formalization and\u0000visualization of hyperbolic geometry\"; Zlatan Magajna, University of Ljubljana,\u0000Slovenia, \"Workshop OK Geometry\"","PeriodicalId":501256,"journal":{"name":"arXiv - CS - Mathematical Software","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139515030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient N-to-M Checkpointing Algorithm for Finite Element Simulations 有限元模拟的高效 N 对 M 检查点算法

arXiv - CS - Mathematical Software Pub Date : 2024-01-11 DOI: arxiv-2401.05868

David A. Ham, Vaclav Hapla, Matthew G. Knepley, Lawrence Mitchell, Koki Sagiyama

引用次数: 0

Toward a comprehensive simulation framework for hypergraphs: a Python-base approach 超图综合仿真框架：基于 Python 的方法

arXiv - CS - Mathematical Software Pub Date : 2024-01-08 DOI: arxiv-2401.03917

Quoc Chuong Nguyen, Trung Kien Le

引用次数: 0

The Cytnx Library for Tensor Networks 用于张量网络的 Cytnx 库

arXiv - CS - Mathematical Software Pub Date : 2024-01-03 DOI: arxiv-2401.01921

Kai-Hsin Wu, Chang-Teng Lin, Ke Hsu, Hao-Ti Hung, Manuel Schneider, Chia-Min Chung, Ying-Jer Kao, Pochung Chen

引用次数: 0

KAXAI: An Integrated Environment for Knowledge Analysis and Explainable AI KAXAI：知识分析和可解释人工智能综合环境

arXiv - CS - Mathematical Software Pub Date : 2023-12-30 DOI: arxiv-2401.00193

Saikat Barua, Dr. Sifat Momen

{"title":"KAXAI: An Integrated Environment for Knowledge Analysis and Explainable AI","authors":"Saikat Barua, Dr. Sifat Momen","doi":"arxiv-2401.00193","DOIUrl":"https://doi.org/arxiv-2401.00193","url":null,"abstract":"In order to fully harness the potential of machine learning, it is crucial to\u0000establish a system that renders the field more accessible and less daunting for\u0000individuals who may not possess a comprehensive understanding of its\u0000intricacies. The paper describes the design of a system that integrates AutoML,\u0000XAI, and synthetic data generation to provide a great UX design for users. The\u0000system allows users to navigate and harness the power of machine learning while\u0000abstracting its complexities and providing high usability. The paper proposes\u0000two novel classifiers, Logistic Regression Forest and Support Vector Tree, for\u0000enhanced model performance, achieving 96% accuracy on a diabetes dataset and\u000093% on a survey dataset. The paper also introduces a model-dependent local\u0000interpreter called MEDLEY and evaluates its interpretation against LIME,\u0000Greedy, and Parzen. Additionally, the paper introduces LLM-based synthetic data\u0000generation, library-based data generation, and enhancing the original dataset\u0000with GAN. The findings on synthetic data suggest that enhancing the original\u0000dataset with GAN is the most reliable way to generate synthetic data, as\u0000evidenced by KS tests, standard deviation, and feature importance. The authors\u0000also found that GAN works best for quantitative datasets.","PeriodicalId":501256,"journal":{"name":"arXiv - CS - Mathematical Software","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139079383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ricci-Notation Tensor Framework for Model-Based Approaches to Imaging 基于模型的成像方法的 Ricci-Notation 张量框架

arXiv - CS - Mathematical Software Pub Date : 2023-12-07 DOI: arxiv-2312.04018

Dileepan JosephElectrical and Computer Engineering, University of Alberta

{"title":"Ricci-Notation Tensor Framework for Model-Based Approaches to Imaging","authors":"Dileepan JosephElectrical and Computer Engineering, University of Alberta","doi":"arxiv-2312.04018","DOIUrl":"https://doi.org/arxiv-2312.04018","url":null,"abstract":"Model-based approaches to imaging, like specialized image enhancements in\u0000astronomy, favour physics-based models which facilitate explanations of\u0000relationships between observed inputs and computed outputs. While this paper\u0000features a tutorial example, inspired by exoplanet imaging, that reveals\u0000embedded 2D fast Fourier transforms in an image enhancement model, the work is\u0000actually about the tensor algebra and software, or tensor frameworks, available\u0000for model-based imaging. The paper proposes a Ricci-notation tensor (RT)\u0000framework, comprising an extended Ricci notation, which aligns well with the\u0000symbolic dual-index algebra of non-Euclidean geometry, and codesigned\u0000object-oriented software, called the RTToolbox for MATLAB. Extensions offer\u0000novel representations for entrywise, pagewise, and broadcasting operations\u0000popular in extended matrix-vector (EMV) frameworks for imaging. Complementing\u0000the EMV algebra computable with MATLAB, the RTToolbox demonstrates programmatic\u0000and computational efficiency thanks to careful design of tensor and dual-index\u0000classes. Compared to a numeric tensor predecessor, the RT framework enables\u0000superior ways to model imaging problems and, thereby, to develop solutions.","PeriodicalId":501256,"journal":{"name":"arXiv - CS - Mathematical Software","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138552293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A New Challenging Curve Fitting Benchmark Test Set for Global Optimization Solvers 一种新的具有挑战性的曲线拟合基准测试集的全局优化求解器

arXiv - CS - Mathematical Software Pub Date : 2023-12-04 DOI: arxiv-2312.01709

Peicong Cheng, Peicheng Cheng

{"title":"A New Challenging Curve Fitting Benchmark Test Set for Global Optimization Solvers","authors":"Peicong Cheng, Peicheng Cheng","doi":"arxiv-2312.01709","DOIUrl":"https://doi.org/arxiv-2312.01709","url":null,"abstract":"Benchmark sets are extremely important for evaluating and developing global\u0000optimization algorithms and related solvers. A new test set named PCC benchmark\u0000is proposed especially for optimization problem of nonlinear curve fitting for\u0000the first time, with the aspiration of investigating and comparing the\u0000performance of different global optimization solvers. Compared with the\u0000well-known classical nonlinear curve fitting benchmark set given by the\u0000National Institute of Standards and Technology (NIST) of USA, the most\u0000important features of the PCC benchmark are small problem dimensions, free\u0000search domain and high level of difficulty for obtaining global optimization\u0000solutions, which makes the PCC benchmark be not only suitable for validating\u0000the effectiveness of various global optimization algorithms, but also more\u0000ideal for verifying and comparing various solvers with global optimization\u0000solving capabilities. Based on PCC and NIST benchmark, seven of the world's\u0000leading global optimization solvers, including Baron, Antigone, Couenne, Lingo,\u0000Scip, Matlab GA and 1stOpt, are thoroughly tested and compared in terms of both\u0000effectiveness and efficiency. The results showed that the NIST benchmark is\u0000relatively simple and not suitable for global optimization testing, while the\u0000PCC benchmark is a unique, challengeable and effective test dataset for testing\u0000and verifying global optimization algorithms and related solvers. 1stOpt solver\u0000gives the overall best performance in both NIST and PCC benchmark.","PeriodicalId":501256,"journal":{"name":"arXiv - CS - Mathematical Software","volume":"19 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138521074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0